When continuous industrial processes are operated near their economic optimum, they are operated at maximum or minimum limits of key operating parameters, such as the product quality specification. Consequently, knowing the current and expected future value of these parameters is very important to both the efficient operation of continuous industrial processes, such as refineries and chemical plants, as well as the prevention of abnormal events. For example, abnormal quality excursions can cause products to be outside their specification limits, cause the sudden malfunctioning of process equipment (such as pump cavitation due to vapor formation), and cause the degradation of process performance (such as loss of reaction from coke buildup on catalyst or loss of heat transfer from coke formation in furnace tubes).
The direct measurement of process stream quality and other key operating parameters can be both expensive and trouble prone. On-line analysis incurs both a high initial installation cost and a high maintenance cost. The on-line analysis often requires a dedicated process sampling system and an environmentally protected field shelter for the analysis equipment. Maintenance of this equipment can require specially trained personnel and high preventative maintenance effort; however it is often the case that maintenance is done only in response to a known problem with the on-line analyzers. Recent on-line analyzer systems incorporate standard samples for testing and calibration, and micro computers which run continual equipment diagnostics.
Often sites choose to make quality measurements using a laboratory analysis, either in conjunction with an on-line analysis or instead of an on-line analysis. Because of the extensive human involvement in taking field samples and then analyzing these samples, these lab analyses are usually infrequent (from daily to weekly), have significant normal variability, and have a high error rate.
To supplement the on-line analysis and laboratory analysis approaches, an inferential estimate of the quality parameter can be created from more readily available process measurements (primarily temperatures, pressures, and flows). The two traditional uses for inferential measurements are first to create a continuous estimate for the more slowly sampled analyzer value for use within closed loop process control applications, and second to validate analyzer and laboratory values. For these uses, by quickly updating the models with the actual on-line analyzer values or laboratory measurements, reasonably adequate performance can be achieved even with poor performing models. If the model has some power to estimate the next analyzer sample, it would behave no worse than using the last analyzer sample as an estimate for the next analyzer sample. However, except for ensuring new analyzer sample values are within minimum and maximum change limits, models that use rapid updating are inadequate for detecting abnormal analyzer sample values or for predicting abnormal quality excursions because of abnormal process events.
For these uses, there cannot be any issue distinguishing a real abnormal event from a defect in the model. This requires that only highest quality training data be used to build the model.
The majority of inferential measurements in the continuous process industries are developed by using process data driven methods such as neural nets, stepwise regression, partial least squares etc. where both the model structure and the model parameters are determined from operating data. Alternatively inferential measurements can be based on first principles engineering models where only the model parameters are determined from operating data. The quality of the models developed using these approaches is significantly affected by the quality of the data selected to build the model or to fit parameters in a first principles engineering model. The data selection, data analysis and data conditioning methods need to be tailored to the characteristics of data, rather than relying on generic approaches based on simple statistical assumptions. The failure to develop high quality inferential estimates in the continuous process industries can often be traced to ineffective data selection and data conditioning methods that don't match the characteristics of process data.